Adherence to and Persistence with Antidepressant Medication during Pregnancy: Does It Differ by the Class of Antidepressant Medication Prescribed?
Bibliographic record
Abstract
OBJECTIVE: Pregnant women are often concerned about the impact of medication use on their pregnancy, such as congenital abnormalities. This study examined the rate of adherence to and persistence with antidepressant medications during pregnancy based on the class of antidepressants prescribed. METHODS: Women who gave birth between 2012 and 2015 in Alberta, Canada; had ≥1 diagnosis of depression within 1 year of preconception in outpatient physician claims, emergency department, or hospitalization administrative data; and were adherent (medication possession ratio ≥80%) to ≥2 consecutive antidepressant prescriptions during the preconception year ( n = 1865) were included in this retrospective cohort study. The rates of adherence and persistence (prescription refill gap ≤30 days) were calculated by antidepressant class and were compared using chi-square tests. RESULTS: During pregnancy, 834 (44.7%; 95% CI, 42.4% to 47.0%) women discontinued antidepressants. Among those continuing antidepressants, the overall rate of adherence was 62.6% (95% CI, 59.4% to 65.7%). The rate differed significantly by medication class ( P < 0.0001), with a rate of 75.1% (95% CI, 68.3% to 80.9%) for serotonin-norepinephrine inhibitors, 60.9% (95% CI, 57.2% to 64.5%) for selective serotonin reuptake inhibitors, 42.8% (95% CI, 19.9% to 69.3%) for nonselective monoamine reuptake inhibitors, and 37.5% (95% CI, 22.5% to 55.4%) for atypical antidepressants. Only, 40.7% (95% CI, 37.5 to 44.1) of women were persistent with antidepressants for the full pregnancy period-the rate differed significantly by medication class ( P < 0.0001). CONCLUSIONS: Adherence to and persistence with antidepressants is low during pregnancy and varies by medication class. Low adherence and persistence can interfere with a therapeutic effect of antidepressants, which may contribute to the worsening of depression symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".